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3D Annotation-Tree Segmentation for Biomedical Engineering [suitable for remote work]




20: Biological Engineering

Faculty Supervisor:

Alan Jasanoff

Faculty email:


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Project Description

Data in Magnetic Resonance Imaging (MRI), the cutting edge of in vivo whole-brain imaging techniques, is commonly acquired at the voxel level. Data *evaluation*, however, requires the integration of multimodal and a priori known information, and this is best done at the segmentation level — also called the “parcellation” or “ROI” (region of interest) level. As part of this project, you will leverage the state-of-the-art brain segmentation tree developed by the Allan Brain Institute in order to establish dynamic brain region classification. You will be tutored in scientific software development technologies (including Git, Python, and Gentoo Linux), as well as in key concepts for efficient, transparent, and sustainable workflow design (including reproducibility, dependency resolution, package management, and collaborative coding), and be guided through the process of applying these skills to the biomedical engineering task at hand. Your work will include interfacing with a web-based API, programming in Python, using cutting-edge neuroimaging libraries such as nibabel and ANTs, managing data resources, and ensuring that all resources can be used reproducibly. Please contact Horea Christian directly.


To adequately tackle the challenges of this project you should have: • prior experience or significant interest in figuring out how to interact with a web-based API • some previous experience working on Linux • some previous experience coding in Python • a keen interest in software transparency and research reproducibility Though not mandatory or assumed, the following would be a significant plus: • prior experience with the Gentoo Linux distribution • prior experience with Git • prior experience with MRI data